State estimation of neural networks with time-varying delays and Markovian jumping parameter based on passivity theory
- State estimation of neural networks with time-varying delays and Markovian jumping parameter based on passivity theory
- 박주현; 정호열; 락쉬마난; D.H. Ji[D.H. Ji]; G. Nagamani[G. Nagamani]
- GLOBAL EXPONENTIAL STABILITY; NEUTRAL-TYPE; DISTRIBUTED DELAYS; ROBUST STABILITY; NONLINEAR-SYSTEMS; DISCRETE; DESIGN; SYNCHRONIZATION; CRITERION; ARRAYS
- Issue Date
- NONLINEAR DYNAMICS, v.70, no.2, pp.1421 - 1434
- In this paper, the state estimation problem is investigated for neural networks with time-varying delays and Markovian jumping parameter based on passivity theory. The neural networks have a finite number of modes and the modes may jump from one to another according to a Markov chain. The main purpose is to estimate the neuron states, through available output measurements such that for all admissible time-delays, the dynamics of the estimation error is globally stable in the mean square and passive from the control input to the output error. Based on the new Lyapunov-Krasovskii functional and passivity theory, delay-dependent conditions are obtained in terms of linear matrix inequalities (LMIs). Finally, a numerical example is provided to demonstrate effectiveness of the proposed method and results.
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공과대학 > 모바일정보통신공학과 > Articles
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